{"id":"W2890808209","doi":"10.5539/ijef.v10n10p1","title":"Efficient Urbanization for Mexican Development","year":2018,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Urbanization; Directory; Population; Metropolitan area; Government (linguistics); Context (archaeology); Data science; Regional science; Economic growth; Computer science; Economics; Geography; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006341091,0.000228275,0.0003141425,0.001082975,0.0006807321,0.002630987,0.0002401537,0.0004328004,0.0108503],"category_scores_gemma":[0.00531271,0.0001620616,0.0004724082,0.001737256,0.0008022644,0.00143529,0.001141483,0.0006202249,0.0003494659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002780027,"about_ca_system_score_gemma":0.001715852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02552625,"about_ca_topic_score_gemma":0.03221136,"domain_scores_codex":[0.999724,0.0000913669,0.00001640285,0.00006614025,0.00004428271,0.00005772421],"domain_scores_gemma":[0.9990855,0.0003754799,0.0002702464,0.0001068747,0.0001089622,0.00005293128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001729728,0.00007624683,0.1363761,0.0003603776,0.0001009954,0.0006417077,0.001224453,0.0959695,0.0009492711,0.6553774,0.01742818,0.09132276],"study_design_scores_gemma":[0.00009493185,0.00007755031,0.1994693,0.0003844227,0.0001338418,0.0003108831,0.006301393,0.2092016,0.001078187,0.4951722,0.0877097,0.00006594967],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8143125,0.003068999,0.03301865,0.01438569,0.0001362952,0.00009784169,0.007847047,0.0005292604,0.1266038],"genre_scores_gemma":[0.9900355,0.0008085694,0.005515939,0.00005628294,0.00003515646,0.00003874354,0.0009098913,0.0000343269,0.002565694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02552625,"threshold_uncertainty_score":0.05075538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335498309967314,"score_gpt":0.2181260823201421,"score_spread":0.1947710992204689,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}